• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1066-1072
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• 2011

In the method to analyze the relationship in the system call orders, the normal system call orders are divided into a certain size of system call orders to generates gene and use them as the detectors. In the method to consider the system call parameters, the mean and standard deviation of the parameter lengths are used as the detectors. The attack of which system call order is normal but the parameter values are changed, such as the format string attack, cannot be detected by the method that considers only the system call orders, whereas the model that considers only the system call parameters has the drawback of high positive defect rate because of the information obtained from the interval where the attack has not been initiated, since the parameters are considered individually. To solve these problems, it is necessary to develop a more efficient learning and detecting method that groups the continuous system call orders and parameters as the approach that considers various characteristics of system call related to attacking simultaneously. In this article, we detected the anomaly of the system call orders and parameters by applying the temporal concept to the system call orders and parameters in order to improve the rate of positive defect, that is, the misjudgment of anomaly as normality. The result of the experiment where the DARPA data set was employed showed that the proposed method improved the positive defect rate by 13% in the system call order model where time was considered in comparison with that of the model where time was not considered.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.137-152
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• 2018

It is a measure to overcome limitations that occur in the activity of detecting and blocking abnormal information leakage activity by collecting the activity log generated by the security solution to detect the leakage of existing financial information and analyzing it by pattern analysis. First, it monitors real-time execution programs in PC that are used as information leakage path (read from the outside, save to the outside, transfer to the outside, etc.) in the PC. Second, it determines whether it is a normal controlled exception control circumvention by interacting with the related security control process at the time the program is executed. Finally, we propose a risk management model that can control the risk of financial information leakage through the process procedure created on the basis of scenario.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1181-1191
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• 2021

The pandemic has rapidly developed a non-face-to-face environment. However, the sudden transition to a non-face-to-face environment has led to new security issues in various areas. One of the new security issues is the security threat of insiders, and the zero trust security model is drawing attention again as a technology to defend against it.. Software Defined Perimeter (SDP) technology consists of various security factors, of which device validation is a technology that can realize zerotrust by monitoring insider usage behavior. But the current SDP specification does not provide a technology that can perform device validation.. Therefore, this paper proposes a device validation technology using SVDD-based abnormal behavior detection technology through user behavior monitoring in an SDP environment and presents a way to perform the device validation technology in the SDP environment by conducting performance evaluation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.309-311
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• 2017

With the recent development of the Internet (IoT) technology, we have come to a very connected society. This paper focuses on the security aspects that can occur within the LPWA Network environment of the Internet of things, and proposes a new machine learning method considering next generation IPS / IDS that can detect and block unexpected and unusual behavior of devices.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.665-666
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• 2009

정보통신 기술과 저장 매체의 발전으로 많은 분야에 편리함과 더불어 산업기밀유출사고의 위험이 늘어나고 있다. 보안사고 중 80% 이상이 인적 보안 유출 이였으며 현직 직원의 유출은 약 25%정도의 부분을 차지하고 있었다. 기존의 단순한 시스템 로그 정보를 이용한 사용자 감사기술, DRM을 이용한 데이터 보호기술방법 보다는 진보된 방법이 필요하다. 사용자 정보와 시스템 정보, 시스템 콜 정보 수집을 통한 구분된 감사데이터의 통계기법을 이용한 지능적인 이상행위 탐지 기법을 제시한다.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.45-51
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• 2018

As data utilization and importance becomes important, data-related accidents and damages are gradually increasing. Especially, insider threats are the most harmful threats. And these insider threats are difficult to detect by traditional security systems, so rule-based abnormal behavior detection method has been widely used. However, it has a lack of adapting flexibly to changes in new attacks and new environments. Therefore, in this paper, we propose an adaptive anomaly movement detection framework based on a statistical Markov model to detect insider threats in advance. This is designed to minimize false positive rate and false negative rate by adopting environment factors that directly influence the behavior, and learning data based on statistical Markov model. In the experimentation, the framework shows good performance with a high F2-score of 0.92 and suspicious behavior detection, which seen as a normal behavior usually. It is also extendable to detect various types of suspicious activities by applying multiple modeling algorithms based on statistical learning and environment factors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2171-2180
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• 2011

Security is a significant concern for many sensor network applications. Intrusion detection is one method of defending against attacks. However, standard intrusion detection techniques are not suitable for sensor networks with limited resources. In this paper, propose a new method for selecting and managing the detect nodes in IDS(intrusion detection system) for anomaly detection in sensor networks and the node scheduling technique for maximizing the IDS's lifetime. Using the genetic algorithm, developed the solutions for suggested optimization equation and verify the effectiveness of proposed methods by simulations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.953-959
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• 2012

There are a number of idle nodes in sensor networks, these can act as detector nodes for anomaly detection in the network. For detecting node selection problem modeled as optimization equation, the conventional method using centralized genetic algorithm was evaluated. In this paper, a method to improve the convergence of the optimal value, while improving energy efficiency as a method of considering the characteristics of the network topology using parallel genetic algorithm is proposed. Through simulation, the proposed method compared with the conventional approaches to the convergence of the optimal value was improved and was found to be energy efficient.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.321-330
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• 2019

Prevention of corporate confidentiality leakage by insiders in enterprises is an essential task for the survival of enterprises. In order to prevent information leakage by insiders, companies have adopted security solutions, but there is a limit to effectively detect abnormal behavior of insiders with access privileges. In this study, we use the Unsupervised Learning algorithm of the machine learning technique to effectively and efficiently cluster the normal and abnormal access logs of the worker's work screen in the manufacturing information system, which includes the company's product manufacturing history and quality information. We propose an optimal feature selection model for anomaly detection by studying clustering methods.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.45-56
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• 2005

The weak foundation of the computing environment caused information leakage and hacking to be uncontrollable, Therefore, dynamic control of security threats and real-time reaction to identical or similar types of accidents after intrusion are considered to be important, h one of the solutions to solve the problem, studies on intrusion detection systems are actively being conducted. To improve the anomaly IDS using system calls, this study focuses on neural networks learning using the soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern, That Is, by changing variable length sequential system call data into a fixed iength behavior pattern using the soundex algorithm, this study conducted neural networks learning by using a backpropagation algorithm. The backpropagation neural networks technique is applied for anomaly detection of system calls using Sendmail Data of UNM to demonstrate its performance.